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Paleoenvironmental reconstructions are critical to determine past climatological and hydrological conditions, such as sea-level rise (SLR) and extreme events including hurricanes and tsunamis. While established quantitative methods, such as principal components analysis and discriminant analysis, have effectively aided reconstructions by demarcating stratigraphic zones, they suffer from limitations due to the underlying assumptions (linearity, normality). Here, we introduce the machine learning technique anomaly detection for sedimentological reconstructions , capable of objectively pinpointing events (anomalies) in sediment cores. We tested this technique on five sediment cores extracted from Laguna Boquita, located along the Pacific coast of Mexico. Each core was subjected to high resolution loss-on-ignition, while the most representative core (core 1) was deemed the training core and scanned with a handheld XRF unit. In general, the sediment cores were dominated by thick units of peat and/or clay, and most cores contained sand layers. This reconstruction represents many environmental settings, ranging from a sandy terrestrial environment (∼6830- ∼ 6370 cal yr BP), organic-rich wetland (∼6370- ∼ 5170 cal yr BP), and clastic-rich backbarrier lagoon (∼5170 cal yr BP to present). The anomaly detection technique proved effective in marking many events, including marine transgression, evidence of SLR, transitions separating dominant depositional environments, and, perhaps most notably, a faded blue clay layer with minimal LOI variability that may represent a shift in backbarrier water level. The anomaly detection also registered anomalies when events were not visually distinct nor present in the LOI datasets (false positives), which represent sediment core sections that require further investigation with multiple proxies. However, anomaly detection failed to register anomalies in certain core sections that should have registered as events due to their distinct nature. Future efforts will look to improve anomaly detection by choosing different train cores and adding additional proxy datasets. • We introduce an anomaly detection technique to sedimentological reconstruction. • The anomaly detection technique was applied to loss-on-ignition datasets. • The anomaly detection technique was useful in detecting anomalies (events). • The anomaly detection technique also registered false positive and false negatives. • Future efforts will focus on analyzing more proxies and trying new train cores. [ABSTRACT FROM AUTHOR]